Actual Measurement Data Research Articles

LESS Spark Ignition Engine: An Innovative Alternative to the Crankshaft Mechanism

In recent years, the internal combustion engine has been the subject of debate mainly concerning its environmental impact. Despite all the discussion it becomes clear day by day that combustion engines will continue to occupy their dominant role over the following decades, especially in the mid- and large-size power spectrum ranges and retain a large share of the market in the smaller-size segment of their application. In this context, in the present paper, a novel engine kinematic mechanism is introduced, which converts rotary to reciprocating motion, and aims to become a potential replacement for the traditional crankshaft mechanism of piston engines. Following a description of the fundamental principles of the new design, we detail the main problems with the application of the new design in the first prototype SI engine and the actions and improvements implemented to overcome them. The actual measurement data from basic engine performance parameters are provided and evaluated, leading to conclusions and decisions for further action which should be implemented in the next improvement steps. Overall, the new SI engine, implementing the novel kinematic mechanism, seems to be quite promising especially in hybrid automotive applications, a fact that encourages the implementation of further improvement plans.

Modelling and Energy Optimization of a Thermal Power Plant Using a Multi-Layer Perception Regression Method

This research paper studies a thermal power plant model with an Artificial Neural Network that contributes to the accuracy improvement of actual measurement data. Neural Networks process the paradigm of algebraic expressions, and their training occurs via a Feed-Forward Back Propagation algorithm implemented in a MATLAB environment. The applied training case in a thermal power plant in Paracha includes three different algorithms, the Levenberg-Marquadt, the Scaled Conjugate Gradient, and the Bayesian Regularization, considering less number of samples to achieve more reliable results. The outcome highlights Bayesian Regularization Networks’ superiority in accuracy and performance compared to Levenberg-Marquadt and the Scaled Conjugate Gradient. The regression analysis estimates the relationship between input-independent and output-dependent variables, forecasts the energetic data, and highlights the benefits of the Bayesian Regularization method in the energy sector.

Identification and correction of abnormal measurement data in power system based on graph convolutional network and gated recurrent unit

Power system measurement data is used as the basis for various power system analyses. Its accuracy and reliability are particularly important. However, due to many problems, there are abnormal data in the measurement data. An identification method based on the spatial-temporal characteristics of measurement data is proposed to resolve the problems of poor identification and low efficiency. The Pre-identification (PI) process used to improve efficiency is introduced, and the sliding window based on the slope is used to realize rapid interval targeting of abnormal data and generate suspicious data sets. A spatiotemporal fusion model based on Graph Convolutional Network (GCN) and Gated Recurrent Unit (GRU) has been proposed. By aggregating the spatial-temporal characteristics of measurement data, precise reconstruction of measurement data is achieved. Then, by setting a threshold to separate the real abnormal data and normal data, the suspicious data set can be cleaned. By simulation experiments, under the cases of different ratios and different types of abnormal data, the proposed method is proved to be better in identification performances and higher in efficiency. By testing actual measurement data, the proposed method can accurately identify abnormal data under the interference of fluctuating data, indicating the proposed method has good robustness.

Research on the Energy Management Strategy of a Hybrid Energy Storage Type Railway Power Conditioner System

High-speed railways generate a large amount of regenerative braking energy during operation but this energy is not utilized efficiently. In order to realize the recycling of regenerative braking energy of high-speed railways, the hybrid energy storage type railway power conditioner (RPC) system is proposed. The working principle and the control strategy of the system are studied. The energy management strategy consisting of a hybrid energy storage system charging and discharging strategy and variational modal decomposition (VMD) power allocation strategy is proposed. Three system operation modes are proposed: the power of the hybrid energy storage system is decomposed by VMD and an interrelationship number is proposed to determine the lithium battery and supercapacitor power. The hardware-in-the-loop test experiments are conducted by the StarSim power electronics small-step real-time simulator from Modeling Tech and the validation analysis is carried out on MATLAB/Simulink with the actual measurement data of a traction substation on the Lanzhou–Xinjiang line. The results verify that the proposed strategy can effectively recycle the regenerative braking energy, realize the peak-shaving effect on the load, and reduce the energy consumption of the train.

Estimating losses at 40-GHz downlink using non-meteorological techniques in heavy rain areas

The advancement of satellite communication has arisen tremendously where higher capacity communications systems are needed. Most satellite engineers are shifting to Ka, Q, and V-band upcoming since the low frequencies such as below 10 GHz are already congested. Actual measurement data at millimetre-wave frequencies in tropical regions are minimal. The prediction of rain attenuation at frequencies above 10 GHz is required to determine a reliable fade margin. In this paper, a statistical frequency scaling technique has been developed as an alternative way of estimating rain attenuation. The technique was derived based on the correlation between the attenuation ratio of a higher and lower frequency against the attenuation at a lower frequency. The attenuations from the proposed model were compared to the proposed frequency scaling by International Telecommunication Union-R (ITU-R) as well as the conventional ITU-R rain prediction model. To deliver a reliable model, validation methods have been done using a set of data with different years and locations in tropical regions. A dependent prediction technique with the lowest root mean square error (RMSE) value and error was produced. This technique is beneficial in applying suitable mitigation techniques to moderate rain fade in tropical regions.

To the problem of the need to install thermoregulators on heating appliances in buildings with district heat supply

The article is devoted to the question of the degree of expediency of installing automatic thermostats on heating appliances in buildings with centralized heat supply. The data of actual temperature measurements and the feeling of thermal comfort are given.

Model and prediction relationship of sound velocity and porosity of seafloor sediments

The relationship between sound velocity and porosity in seafloor sediments is widely utilized for predicting or inverting sediment acoustical and physical parameters. This paper analyzes the theoretical relationship between sound velocity and porosity using the General Model of Sound Speed (GMSS). The GMSS model elucidates an empirical equation relationship in the form of a quadratic fitted polynomial based on actual measurement data from the South China Sea. The accuracy of the GMSS model and the regression empirical equation are found to be consistent, as demonstrated by the relative error, absolute error, and standard deviation of the sound velocity predictions. Our findings suggest that if the scattering of measured data is inherent to seafloor sediment, the empirical equation fails to adequately explain sediment variations and lacks broad applicability for accurate prediction and calculation. Additionally, we observe that a ± 5% relative error in porosity has minimal impact on the application of the GMSS model and empirical equations for predicting sound velocity errors. However, the empirical equation, relying solely on the porosity parameter, inadequately accounts for sound velocity scattering in seafloor sediments with the same porosity due to inherent limitations. In contrast, the GMSS model can incorporate additional physical parameters beyond porosity to better explain and predict sound velocity in seafloor sediments scattering in seafloor sediments with the same porosity, albeit with increased complexity. This paper provides an analytical method and theoretical foundation for inverting or predicting the relationship between sound velocity and porosity of seafloor sediments.

Data-Driven Fast Uncertainty Assessment of Distribution Systems With Correlated EV Charging Demand and Renewable Generation

When multiple uncertain resources (e.g., renewable energy and EV charging stations) are integrated into distribution systems, the uncertainties of correlated power supplies and demands pose a challenge for reliable grid operation. This challenge is compounded by the fact that this correlation cannot be modeled by standard pair copulas, e.g., Gaussian, Clayton, and Frank, as observed from the actual measurement data. To address these issues, this paper proposes a data-driven approach based on Gaussian Mixture Model (GMM) and Polynomial Chaos Expansion (PCE) for efficient grid impact studies with multiple correlated uncertain resources. The GMM is applied to segment these uncertain resources into multiple clusters, and the joint probability density within each cluster is approximated by multivariate Gaussian functions. The result is a feasibility for applying Gaussian Copula iteratively to handle the nonlinear correlation and the avoided construction of polynomial bases from arbitrary probability distributions for improved performance. Subsequently, the k-medoids clustering is utilized to strategically select experimental samples of PCE for the fast uncertainty quantification. The high efficiency and accuracy of the proposed approach has been demonstrated with real-world distribution feeder circuits together with actual measurement data from EV charging stations and solar farms.

전력시스템 진동 해석을 위한 RMS 필터링 기반 Prony 분석 차수 선택

This paper aims to investigate RMS filtering and Prony analysis for detecting oscillation frequencies in order to evaluate the stability and reliability of power systems, proposing a method for selecting the polynomial order in Prony analysis. Among the oscillation detection methods using data, Prony analysis is commonly utilized due to its precision and capability of providing abundant information such as oscillation frequency and damping degree, but there is no standard for selecting the polynomial order. The order needs to be selected based on factors such as data volume, sampling interval, and noise level. In this paper, the Hann Window is applied in each section to select the appropriate order and the dominant oscillation mode is estimated through RMS filtering. The order that derives the estimated frequency results is applied in Prony analysis. The proposed method is demonstrated to be effective with known test data and actual measurement data.

The influence of the selection of zero-crossing starting point on measurement accuracy of the absolute gravimeter

The absolute gravimeter is an important precision instrument for obtaining gravity acceleration in fields such as geophysics, resource survey, and national defense construction. It is greatly affected by its self-vibration during operation. If the self-vibration frequency exceeds the range of the sensor, it will not be effectively processed by the method of vibration compensation. To solve this problem, the effect of instrument self-vibration on the accuracy of gravity is analyzed through simulation and actual measurement data. Based on the calculation of the simulation data, the functional relationship between the self-vibration frequency of the instrument and the zero-crossing starting point is obtained. The Starting Point Mathematical Model is established to accurately calculate the zero-crossing starting point. Then, the zero-crossing starting point calculated by the model is taken as the time reference of the zero crossing for the least square fitting. The split and platform absolute gravity measurement systems are built, respectively, to test the measured data. The test results reveal that, compared with the traditional method using the initial falling time as the zero-crossing starting point, the proposed method can reduce the gravity deviation by 50 μGal and improve the accuracy by an average of 35.35% under split-type working conditions. Under platform-type working conditions, it can decrease the gravity deviation by 3 mGal and improve the accuracy by an average of 53.78%. The method proposed can reduce the fixed phase vibration interference caused by the instrument self-vibration and provide a reference for improving the measurement accuracy of the instrument under different working conditions.

Health Condition Identification and Quantitative Assessment of Emergency Diesel Generator Sets Based on Back Propagation Neural Network and Mahalanobis Distance

To address the problem of a comprehensive assessment of emergency diesel generator set health status, a quantitative assessment method of emergency diesel generator set health status based on data-driven and distance measurement is proposed. BP neural network was used to extract the feature sequences of emergency diesel generator sets under different health states. An emergency diesel generator set health state identification model was established. To further quantitatively evaluate the health level of the emergency diesel generator set, the distance between each state of the emergency diesel generator set and the health state is measured by introducing the Mahalanobis distance algorithm, which realizes the unification of both qualitative and quantitative evaluation of the health state of the emergency diesel generator set. The operating states of diesel generator sets were subdivided into four states: healthy, sub-healthy, abnormal, and fault, and a simulation test study was conducted using the actual measurement data set of emergency diesel generator set operation. It is verified that the proposed method can classify the operating status of diesel generator sets more accurately and obtain the health assessment score index. The integration of qualitative and quantitative assessment of the health status of emergency diesel generator sets is realized, and the status warning is carried out accordingly, which provides an accurate basis for taking different maintenance and repair measures in a targeted manner.

SSA research on electromagnetic scattering in arid zone

Abstract This paper investigates the electromagnetic scattering characteristics in arid zone and proposes methods for monitoring and improving ecological agriculture, vegetation landscape and desertification in arid zone, based on the modified small slope approximation (SSA) electromagnetic scattering model. The dielectric properties of dryland soils are studied in the paper, and the variation curves of soil dielectric constant with humidity and electromagnetic frequency are plotted. In order to reproduce the geomorphological features of the dryland areas, a two-dimensional geometric model of the ground environment is established using Monte Carlo methods combined with Gaussian spectral functions. An algorithm model based on the modified SSA is developed, and the algorithm was validated and analyzed to verify its reliability. In the simulations, the southeastern region of Ejina Banner, Inner Mongolia was taken as a typical arid region, and the simulation parameters are obtained from the actual measurement data of the region. The study finds that the electromagnetic scattering characteristics of the arid zone are influenced by soil water content, ground roughness and incident wave electromagnetic frequency, and there is regularity. The results of this paper can be effectively combined with remote sensing technology to contribute to many researches on soil moisture inversion and ecological condition of vegetation, which are important in many aspects such as irrigation control, ecological research, vorticity covariance, and climatology of slope stability.

Inversion of Nutrient Concentrations Using Machine Learning and Influencing Factors in Minjiang River

Remote sensing is widely used for lake-water-quality monitoring, but the inversion of the total nitrogen (TN) and total phosphorus (TP) of rivers and non-optical parameters is still a difficult problem. The use of high spatial and temporal resolution multispectral imagery combined with machine learning techniques is an effective solution for this difficulty. Three machine learning methods based on support vector regression (SVR), neural network (NN) and random forest (RF) were used to invert TN and TP using actual water-quality measurement data and Sentine-2 remote-sensing images, and analyzed the factors influencing water quality in terms of pollutant emissions and land use. The results show that RF performs the best in both TN (R2 = 0.800, RMSE = 0.640, MSE = 0.400, MAE = 0.480) and TP (R2 = 0.830, RMSE = 0.033, MSE = 0.001, MAE = 0.022) inversion models, and that the optimal selection of feature variables improves model performance. The TN and TP concentrations in the Minjiang River Meishan Water Function Development Zone were the highest in the downstream section and in 2018. Analysis of the factors influencing water quality shows that pollution sources and amounts were closely related to land-use types, and land use in riparian zones at different spatial scales had different degrees of impact on water quality.

The Use of Evolutionary Algorithms in the Modelling of Diffuse Radiation in Terms of Simulating the Energy Efficiency of Photovoltaic Systems

In light of the rapidly growing number of photovoltaic micro-grids, the modelling of their short-term power yields based on meteorological measurements is increasing in significance. This requires the knowledge of total and diffuse instantaneous solar radiation; however, most meteorological stations conduct actinometric measurements only with regard to total solar radiation, especially on a minute scale. This paper contains an analysis of the currently used PV cell mathematical model and suggests its modification aimed at calculating PV cell power with satisfactory accuracy, without the knowledge of diffuse solar radiation. Three function families were proposed to approximate the relationship between diffuse irradiance and the total and theoretical total irradiance variance for a cloudless sky. A program has been implemented to identify functions from the aforementioned function families. It leverages an evolution strategy algorithm and a fitness function based on the least-squares point method. It was employed to calculate the desired functions based on actual measurement data. The outcome was the sought-after dependence that enables predicting diffuse irradiance based on more frequently available measurement data.

RESEARCH ON 3D RECONSTRUCTION METHOD AND APPLICATION OF FOOD IN STROKE PATIENTS BASED ON RGB-D IMAGE

RGB-D images can be used to build 3D models with high precision and efficiency. This paper introduces the method and principle of 3D modeling and the 3D reconstruction process of RGB-D images and analyzes KinectFusion and BundleFusion methods. Based on BundleFusion, this paper constructs a set of 3D reconstruction methods for stroke patients’ food and carries out 3D reconstruction experiments on watermelon, banana, orange, corn, and dish cold bitter gourd. With the actual measurement data validation, the banana’s average error rate of 3.95%; for watermelon, the average error rate is 3.89%; for grapes, the average error rate is 4.28%; for corn, apple, banana mixture composition, the average error rate is 2.61%, the average error rate of caramel treats is 3.48%, and the average error rate of pork floss bread is 2.85%. The average error rate of the hot and dry noodles is 1.85%, and the average error rate of the breakfast mix composition is 2.91%. The error rate in subsequent food volume calculation, quality estimation and calculation application is within the effective range of nutrients. It provides a technical basis and scientific basis for dietary intervention of stroke patients.

전기저장장치의 배터리 기생커패시턴스 및 SOC에 따른 절연저항 변동 메커니즘 및 모델링에 관한 연구

Recently, due to the significant growth of RES(renewable energy source), the capacity of ESS (energy storage system) interconnected to RES is increasing rapidly. Especially, it is mandatory to install IMD (insulation monitoring device) in order to monitor insulation resistance of ESS and detect faults such as ground fault. However, there are no specific regulations on numerous IMD being operated in actual ESS, and particularly the issues that insulation resistance dramatically varies depending on the stray capacitance and SOC (state of charge) of batteries are being occurred in large scaled ESS. Therefore, in order to operate ESS in a stable manner, this paper performs surveys on actual measurement data of ESS insulation resistance, which varies according to the operation environment and conditions in distribution system. Based on the evaluation on influence of stray capacitance and SOC to insulation resistance from the survey results, this paper proposes a mechanism of variation in insulation resistance depending on the stray capacitance and SOC of battery and performs modeling of DC distribution system with ESS to evaluate the variation characteristics of insulation resistance according to each mechanism. From the simulation result based on the proposed mechanism and modeling, it is confirmed that the leakage current and CMV (common mode voltage) increases along with stray capacitance and SOC but insulation resistance decreases. Therefore, in order to operate large scaled ESS in a stable manner, it is confirmed that proper measures to enhance insulation level depending on the capacity and SOC of battery and mitigate leakage current and CMV.

Analysis on The Effect of Channel Bandwidth Occupying in LTE Frequency Band on Throughput

The maximum channel capacity of a 4G LTE network will be directly correlated to the channel bandwidth that occupies a certain radio frequency on that network. The number of Resource Blocks (RB), subcarriers, and Resource Elements (RE) as channel resources available to serve users concurrently will depend on the bandwidth channel sizes. Mobile service providers in Indonesia provide scalable channel bandwidth LTE 4G networks on a variety of frequency bands, including adjacent and non-adjacent bands. In this study, it was examined how to channel bandwidth, which changes at 10MHz, 15MHz, and 20MHz, affects throughput to give user equipment the best possible performance (UE). This study makes use of actual measurement data from installed and active eNodeB or cells obtained via the drive test approach. According to the test, each channel's maximum throughput varies, however, there is no linear relationship found between channel bandwidth expansion and throughput. The 20MHz channel bandwidth, which is located in the 2300MHz frequency band, has a maximum throughput value of 64666.80 kbps.

Research on the influential characteristics of asymmetric wind fields on typhoon waves

The East China Sea is an ocean region with frequent typhoons, typhoons are also the main reason for inducing typhoon waves. The complexity of typhoon waves is closely related to the complexity of typhoon wind field. In previous studies, symmetrical wind field models or superimposed wind field models were usually used to simulate typhoon waves. However, the actual wind fields are asymmetric, and the asymmetry is affected by many factors. Therefore, three wind field models are used to simulate the wind field of Typhoon Muifa that moved through the East China Sea. Moreover, the abovementioned wind field models are used to drive the third-generation wave model SWAN to simulate the wave field and wave spectrum of Typhoon Muifa. Studies show that the values generated by the asymmetric wind field model are most consistent with the actual measurement data. This is especially the case if the typhoon center is closer to the station. The accuracy of the typhoon waves simulated using the asymmetric wind field model is better than that of the other two wind field models. The asymmetric wind field can reflect the asymmetric characteristics of the typhoon well. The substantial wave heights on the right side of typhoon’s path are substantially higher than those on the left side of path. Additionally, the maximum wave spectral density and total energy of waves as simulated by the asymmetric wind field are both larger than those of the other two wind field models. Thus, the asymmetric wind field model is more suitable for the numerical simulation of typhoon waves in the East China Sea.

An EIT image reconstruction method based on DenseNet with multi-scale convolution.

Electrical impedance tomography (EIT) is an imaging technique that non-invasively acquires the electrical conductivity distribution within a field. The ill-posed and nonlinear nature of the image reconstruction process results in lower quality of the obtained images. To solve this problem, an EIT image reconstruction method based on DenseNet with multi-scale convolution named MS-DenseNet is proposed. In the proposed method, three different multi-scale convolutional dense blocks are incorporated to replace the conventional dense blocks; they are placed in parallel to improve the generalization ability of the network. The connection layer between dense blocks adopts a hybrid pooling structure, which reduces the loss of information in the traditional pooling process. A learning rate setting achieves reduction in two stages and optimizes the fitting ability of the network. The input of the constructed network is the boundary voltage data, and the output is the conductivity distribution of the imaging area. The network was trained and tested on a simulated dataset, and it was further tested using actual measurement data. The images reconstructed via this method were evaluated by employing root mean square error, structural similarity index measure, mean absolute error and image correlation coefficient in comparison with conventional DenseNet and Gauss-Newton. The results show that the method improves the artifact and edge blur problems, achieves higher values on the image metrics and improves the EIT image quality.

Research on operation decision of thermal power units with both electricity spot trading and carbon emissions trading

Low carbon operation is one of the goals of the energy revolution, and the thermal power units are required to achieve more carbon emission reduction for achieving the carbon peaking and carbon neutrality goals. To help thermal power units participating in both electricity spot trading and carbon trading to optimize their operational decisions, a cost-benefit model for thermal power units that take into account both electricity spot trading and carbon trading is built from two perspectives: low-carbon technology and low-carbon policy, combining low-carbon instruments with market mechanisms. The objective is to maximize the profitability of the unit, and the effectiveness of the proposed model is proved by simulation using actual measurement data. The results proves that the proposed model can balance low carbon target and economic aspects, and is also useful for thermal power units to optimize their operational decisions under different scenarios.